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Testing data Well#A.
Presuming the model does not fit the data well, a significant lack of fit will ensue; suggesting that there may be some systematic variation unaccounted by the hypothesized model (Bashir et al. 2010).
After training step was done, the two neural networks were tested using testing data (not trained) from Well A. Figure 5a, b shows cross-plots predicted values versus observed values porosity for testing data (Well#A).
Training data Well#A Table 2 Statistical performance of GRNN and FFBP scheme of porosity and observed porosity (Well#A) Methods Testing data (314 data points) non trained Training data (734 data point) R 2 r MSE R 2 r MSE GRNN 0.958 0.978 0.278 0.970 0.984 0.383 FFBP 0.940 0.969 0.381 0.960 0.979 0.449.
Whilst a (2-parameter) single-exponential [[fub = fub(0) + fub(1)exp -P/P0(1))] dexp -P/P0ur data well, a (5-parameter) double exponential [fub = fub(1)exp(-P/P0(1)) + fub(2)exp(-P/P0(2))] describes our data significantly better, especially at the higher laser powell (fitting statistics presented in supporting Material S1).
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The 1043 data points from Well#A were randomly divided into training data (70%%) and testing data (30%%).
This link function did not model the data (which included new clinical data) well, and a gamma-log link provided a better fit to the data.
The optimized LS-SVM model fits the experimental data well, with a mean squared error of 0.0002 and a squared correlation coefficient of 99.98%.
The model fit the experimental data well, showing a good visual fit to the time courses and a low residual sum of squares (1.3 × 10−11).
A clear linear decay fit the log-log data well (with a R value of 0.78) in the case of GC pairs.
The Langmuir equation fitted the isotherm data well, with a maximum adsorption capacity of 21.70 mg P/g.
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